/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin sparse_hes.cpp$$
$spell
	Cpp
	Hessian
$$

$section Computing Sparse Hessian: Example and Test$$


$code
$srcfile%example/sparse/sparse_hes.cpp%0%// BEGIN C++%// END C++%1%$$
$$

$end
*/
// BEGIN C++
# include <cppad/cppad.hpp>
bool sparse_hes(void)
{	bool ok = true;
	using CppAD::AD;
	using CppAD::NearEqual;
	//
	typedef CPPAD_TESTVECTOR(AD<double>)               a_vector;
	typedef CPPAD_TESTVECTOR(double)                   d_vector;
	typedef CPPAD_TESTVECTOR(size_t)                   s_vector;
	typedef CPPAD_TESTVECTOR(bool)                     b_vector;
	//
	// domain space vector
	size_t n = 12;  // must be greater than or equal 3; see n_sweep below
	a_vector a_x(n);
	for(size_t j = 0; j < n; j++)
		a_x[j] = AD<double> (0);
	//
	// declare independent variables and starting recording
	CppAD::Independent(a_x);
	//
	// range space vector
	size_t m = 1;
	a_vector a_y(m);
	a_y[0] = a_x[0] * a_x[1];
	for(size_t j = 0; j < n; j++)
		a_y[0] += a_x[j] * a_x[j] * a_x[j];
	//
	// create f: x -> y and stop tape recording
	// (without executing zero order forward calculation)
	CppAD::ADFun<double> f;
	f.Dependent(a_x, a_y);
	//
	// new value for the independent variable vector, and weighting vector
	d_vector w(m), x(n);
	for(size_t j = 0; j < n; j++)
		x[j] = double(j);
	w[0] = 1.0;
	//
	// vector used to check the value of the hessian
	d_vector check(n * n);
	size_t ij  = 0 * n + 1;
	for(ij = 0; ij < n * n; ij++)
		check[ij] = 0.0;
	ij         = 0 * n + 1;
	check[ij]  = 1.0;
	ij         = 1 * n + 0;
	check[ij]  = 1.0 ;
	for(size_t j = 0; j < n; j++)
	{	ij = j * n + j;
		check[ij] = 6.0 * x[j];
	}
	//
	// compute Hessian sparsity pattern
	b_vector select_domain(n), select_range(m);
	for(size_t j = 0; j < n; j++)
		select_domain[j] = true;
	select_range[0] = true;
	//
	CppAD::sparse_rc<s_vector> hes_pattern;
	bool internal_bool = false;
	f.for_hes_sparsity(
		select_domain, select_range, internal_bool, hes_pattern
	);
	//
	// compute entire sparse Hessian (really only need lower triangle)
	CppAD::sparse_rcv<s_vector, d_vector> subset( hes_pattern );
	CppAD::sparse_hes_work work;
	std::string coloring = "cppad.symmetric";
	size_t n_sweep = f.sparse_hes(x, w, subset, hes_pattern, coloring, work);
	ok &= n_sweep == 2;
	//
	const s_vector row( subset.row() );
	const s_vector col( subset.col() );
	const d_vector val( subset.val() );
	size_t nnz = subset.nnz();
	ok &= nnz == n + 2;
	for(size_t k = 0; k < nnz; k++)
	{	ij = row[k] * n + col[k];
		ok &= val[k] == check[ij];
	}
	return ok;
}
// END C++
